用简单提示提升零样本异常检测，效果优于复杂方法
TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
- 采用空间感知的TIPS模型替代CLIP，改善图像对齐与敏感度
- 图像级提升1.1%-3.9%，像素级提升1.5%-6.9%，跨7个工业数据集表现优异
- 无需复杂模块，轻量架构即可实现强泛化，适合工业部署
异常检测在安全关键场景中识别行为偏离。当目标域正常数据不可得时，零样本异常检测（ZSAD）依赖视觉语言模型（VLM）。然而，CLIP的粗粒度图文对齐导致定位与检测能力受限，主要源于（i）空间错位和（ii）对细粒度异常敏感度弱；此前工作通过复杂辅助模块补偿，却忽视了骨干模型选择。本文重新审视骨干模型，采用基于空间感知目标训练的TIPS模型。尽管TIPS缓解了CLIP问题，但暴露全局与局部特征分布差异。为此，我们提出解耦提示：固定提示用于图像级检测，可学习提示用于像素级定位，并将局部证据注入全局得分。无需依赖CLIP特有技巧，所提TIPS管道在七个工业数据集上，图像级性能提升1.1%-3.9%，像素级提升1.5%-6.9%，以轻量架构实现强泛化。代码已开源。
原文摘要 · Abstract (English)
Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) leverages vision-language models (VLMs). However, CLIP's coarse image-text alignment limits both localization and detection due to (i) spatial misalignment and (ii) weak sensitivity to fine-grained anomalies; prior works compensate with complex auxiliary modules yet largely overlook the choice of backbone. We revisit the backbone and use TIPS-a VLM trained with spatially aware objectives. While TIPS alleviates CLIP's issues, it exposes a distributional gap between global and local features. We address this with decoupled prompts-fixed for image-level detection and learnable for pixel-level localization-and by injecting local evidence into the global score. Without CLIP-specific tricks, our TIPS-based pipeline improves image-level performance by 1.1-3.9% and pixel-level by 1.5-6.9% across seven industrial datasets, delivering strong generalization with a lean architecture. Code is available at github.com/AlirezaSalehy/Tipsomaly.
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